TaSR-RAG: Taxonomy-guided Structured Reasoning for Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) helps large language models (LLMs) answer knowledge-intensive and time-sensitive questions by conditioning generation on external evidence. However, most RAG systems still retrieve unstructured chunks and rely on one-shot generation, which often yields redundant context, low information density, and brittle multi-hop reasoning. While structured RAG pipelines can improve grounding, they typically require costly and error-prone graph construction or impose rigid entity-centric structures that do not align with the query's reasoning chain. We propose \textsc{TaSR-RAG}, a taxonomy-guided structured reasoning framework for evidence selection. We represent b...
| Authors | Jiashuo Sun, Yixuan Xie, Jimeng Shi et al. |
|---|---|
| Primary category | cs.CL |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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